1. Field of the Invention
The present invention relates to performing surveillance on a biological population for exposure to an agent that acts on members of that population; and in particular to the early detection of localized exposure using cluster analysis on anomalous conditions determined from time series of multiple data types.
2. Description of the Related Art
The past approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not to be considered prior art to the claims in this application merely due to the presence of these approaches in this background section.
Recent history demonstrates that weapons of mass destruction can be built and deployed by almost any individual or group that has intent to cause harm or that is looking chemical and biological agents. These weapons, banned from wartime usage, have nevertheless proliferated in third world countries. Information on the development and deployment of these weapons has become widely available on the Internet. Materials to produce some agents are also readily available. Certain biological agents pose a particularly insidious threat in that a clandestine release into a population may not be noticed during the incubation period of the resultant disease. Yet, concerning agents such as anthrax, once the symptoms are manifested it is no longer possible to treat the victim and high mortality is inevitable. Contagious agents like smallpox or the plague pose even greater threats. Such agents require early identification of an infected population in order to treat the victims and contain a potentially devastating epidemic.
Use of biological weapons therefore poses very serious issues for crisis and consequence management. Various State and local emergency management plans utilize fire, rescue, and law enforcement first responders to provide emergency assistance, to control an incident site, and to collect evidence for criminal prosecution. For clandestine bio-agent releases, the medical community may be the first to see patients present with uncommon diseases. These diseases include small pox, plague, tularemia, anthrax, etc., and have a high mortality rate. In order to institute measures to contain disease outbreaks, public health officials must receive timely reports from agencies and health providers in their jurisdiction. Early warning is key to managing an epidemic and saving lives. However, the first indicators of a bio-terrorist event may be the onset of disease in humans and animals. And professionals from the health care community may not be able to recognize the early signs of diseases that would result from bio-terrorism. Early diagnosis of such diseases is often difficult because the diseases generate only common “flu-like” initial symptoms.
To overcome the obstacles concerning an effective early warning system, improved technology is needed. Information technology and advanced telecommunications can play a major role in improving surveillance for biological and chemical weapons of mass destruction. Information integrated from multiple sources that interface with the health care needs of a community can provide early warning for the onset of an outbreak resulting from terrorist activities. Even seemingly small advances in early warning timing could save a tremendous number of lives.
However, there are significant limitations with previous attempts at constructing early warning bio-surveillance systems. Conventional bio-surveillance focuses on categorical data collected from emergency rooms, clinics, and other healthcare facilities. The detection algorithms in these conventional systems rely on threshold crossing algorithms applied to single streams of data. Such an approach does not make optimal use of available information and cannot detect a bio-terrorist attack until sizeable numbers of infected individuals appear at healthcare facilities.
Further, conventional bio-surveillance is labor-intensive. For an early warning system to be a viable option several processes must be instituted. First, data from multiple agencies that interface with human health, animal health, and agriculture must be collected and forwarded to a central integration facility. In most systems, a human analyst is needed to review all the data received to extract indicators of a bio-terrorist event. If indicators are found, the analyst needs to assemble the knowledge to form an argument. When an argument is sufficiently mature, the analyst must originate alerts to the specific organizations that need to respond to the incident. This form of bio-surveillance requires continuous support, delays alerts and may be cost prohibitive both for the agencies supporting and analyzing the data.
A need exists therefore for automated early warning bio-surveillance detection and alerting system. Such a system should be capable of operating continuously with minimal human intervention, and should exploit the data collection and analysis capabilities of modern information technology and advanced telecommunications.
In one recent approach for a more fully automated early warning system, described in the related PCT application cited above, data from multiple data types indicative of non-specific, flu-like responses to active agents are collected. A background is generated and subtracted from the data to form residuals. The residuals are used with a matched filter to detect exposure of a population to biologically active agents. The matched filter employs replica signals for residuals in the multiple data types based on one or more hypothetical exposure events. The replicas are compared to observed residuals to determine when a match occurs that indicates the likelihood of an actual outbreak similar to the hypothetical event at a given level of significance for a given limit on false alarms. A system based on this recent approach detects an outbreak more rapidly than other approaches that rely on a single data type.
While suitable for many purposes, and offering many advantages over prior approaches, this recent approach also suffers some disadvantages. One disadvantage is that a great deal of processing power is consumed to generate replicas for even a limited region. This consumption inhibits the use of the method over large geographic regions, such as the eastern or western United States.
Another disadvantage is that a larger area is subject to more different phenomena that contribute to variability of the observed data types and thus introduce noise that can mask indications of a localized exposure event. As a consequence, the signal-to-noise ratio (SNR) for the larger area is smaller than the SNR in a smaller area that contains the outbreak. In essence, the signal is diluted over the larger area.
Furthermore, in this recent approach, the background for a particular location is determined using a retinal banding approach that determines the average value of the data at locations around the particular location but excluding the particular location. If the signal encompasses a cluster of several neighboring locations where data are collected, the background computed using this recent approach may contain some of the signal and the computed residual may be smaller than the actual or predicted residual. This can degrade the detection of an actual localized event by the matched filter.
Based on the foregoing description, there is a clear need for an automated early warning bio-surveillance detection and alerting system that can be scaled up to cover larger areas and that does not suffer the disadvantages of the other approaches.